Cognitive Twins: From Digital Representation to Governed Action

Complex organizations rely on fragmented decision systems that create coordination overhead, diffuse accountability, and difficulty scaling institutional knowledge. AI governance frameworks specify desired outcomes but leave open how those outcomes are enforced at runtime. This paper defines the Cognitive Twin, a decision-capable digital representation that executes a continuous G-PAPA decision cycle (Governance, Perception, Assembly, Prediction, Action). Governance is the defining property: embedded policy separates what the system can do from what it is authorized to do, and that separation is enforced at every subsequent step. Perception produces observations under source-authority and quality constraints. Assembly processes admitted observations to maintain a continuously updated world model of the managed entity. Prediction determines what is significant in the assembled state and forecasts how conditions may evolve. Action optimizes across competing objectives and, where authorized, executes. A Cognitive Twin maintains persistent state, processes the full decision cycle, and enforces explicit authority boundaries throughout. The paper contributes:(i) a formal definition with five qualifying criteria (CT-1 through CT-5);(ii) distinctions from related system types; and(iii) a governance-first architecture implemented as a network of Precision Agents with explicit policy checkpoints and auditable provenance. The goal is a precise specification for action-capable systems that remain accountable to institutional constraints.